[D] Jow Growth

Hey guys, junior in university rn, i’ve been hearing a lot about how people think ML will fizzle out in 10 or so years. Do these claims hold any truth? I really want to get into ML and AI and hearing this concerns me submitted by /u/ODFP [link] [comments]

1h

[D] Negative examples are still useful in self-supervised learning even after the BYOL, and they are directly trainable end-to-end with a backbone.

In a recently published paper at https://arxiv.org/abs/2011.08435, a pre-training algorithm called AdCo (Adversarial Contrast) was presented to show the negative examples can be directly trained end-to-end together with the representation backbone. Only an adversarial loss was needed to train these negatives. Conceptually, these negative examples can...

1h

[N] I made a great A.I., did not use machine learning

I did not use neural nets or machine learning. I did it differently, and it passes the Turing Test*. edited to add: on a limited but substantial domain edited to add: and you know my haters and my downvoters, their a.i. can't pass the Test; it's why they downvote submitted by /u/pussyFiller2020 [link] [comments]

2h

[D] Best way to pad and concatenate sequences?

I have a pretty basic question for which i haven't been able to find an answer. I'm creating a character-based text model for generating English language tweets which I'm training using a suitable corpus. My question is: how to best present the training data to the network? I currently concatenate all tweets in the training corpus into one large sequence,...

3h

Analyzing Dataset Consistency [R]

Real world data is often observably consistent over small distances. For example, the temperature on the surface of an object is probably going to be roughly the same over distances that are small relative to its total area. Similarly, the average color in an image of a real world object generally doesn’t change much over small distances, other than...

5h

[Discussion] How many regions of different class does a typical neural network split its input space into?

By definition, a classification model fractures its input space into a number of contiguous regions with different classes. With a one- or two-dimensional input, these regions are easy to visualize; for example, (this)[https://i.imgur.com/cwWvwPe.png] figure shows the class predictions for a neural network trained on 2D toy data. As far as this plot...

5h

[N] LAMA AI's weekly news, updates, and events.

Hey guys! LAMA (https://lamaai.io) is back again with couple of updates for you all. Let's start with this weeks AI news! You can find the video here, but as for the key highlights: Alibaba announce M6 - the largest Chinese pretrained language model OpenAI show multi-modal neuron behaviour in CLIP u/SergiosKar releases a 'Productionising Deep Learning'...

5h

[D] Is there any point to regulating AI development?

I don't see it, and I'm going to explain why below but if there is any errors in my deductions please let me know, always wanting to learn. My though process here is, people like Elon Musk are taking after a school of thought that says the government should regulate development of AI and eventually AGI, slow it down, etc. I guess the motive here would...

8h

[D] Best book (hardcopy) for RL with code implementation ?

Hi everyone, I just finished Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow from A. Géron and wanted to dive deeper into Reinforcement Learning, as the Chapter covers the essential but does not go deeply into all variants and examples of RL. My main focus is to go through concrete examples by trying them but also understand the theory...

8h

[D] [NLP] Did anything significant happen between RNN and transformer approaches?

I got familiar with NLP in 2017 doing Andrew Ng DL specialization. At that time RNN with word embeddings and attention seemed was presented as SOTA. It jump-started my DS path but on regular basis, I am not doing DL at all. I would like to catch up. By doing my research now I see transformers (aka context-free approach?) everywhere. This points me to...

9h